Chemical manufacturing processes typically operate in the liquid or gas phase within a set of operating conditions such as temperature, pressure, and catalyst concentration to produce a material having a desired set of physical and chemical properties.
For example, one or more olefins can be reacted in a liquid or gas phase reactor in the presence of a catalyst to produce a polyolefin or other polymer. A variety of polymers having different properties can be manufactured in the same reactor by altering the operating conditions, types and ratios of reactor feedstock, catalyst and additives. One polymer property often of great interest is polymer melt flow rate.
Modern chemical reactors typically employ computer-based control of some type to maintain product quality and to transition operation from the manufacture of one product to another. Where the reactor is used to manufacture polypropylene, the melt flow rate can be altered if the control program alters, for example, the hydrogen to propylene ratio present in the reactor.
The types of control used in modern reactors can range from one or more control loops using relatively simple proportional integral derivative (PID) or fuzzy logic controllers to sophisticated state of the art predictive control programs. Control systems of modern polypropylene plants used to control the properties and consistency of the manufactured polypropylenes typically are predictive models, and can be of the “first principles” type, semi-empirical type, or completely empirical type.
A first principles model employs process control equations derived from physical and chemical relationships that describe various aspects of the interaction of materials within the process. A semi-empirical model employs equations of the type used in a first principles model but which have been modified by empirical analysis of data to produce a somewhat better result. An empirical model uses relationships derived from observation of the process behavior in an attempt to model the behavior, without any particular regard for the first principles type of equations typically used to describe behavior of materials within the reactor and associated processes. Examples of empirical models include many forms of regression models, including neural networks. In practice, these three model types represent a continuum of models useful for predictive control of the reactor, and most models will exhibit at least some degree of both first principle and empirical concepts.
A goal of most any polymer control system or process model will be to produce a material having a specified set of properties, including polymer melt flow rate. Because loop control and models both tend to represent imperfect descriptions of behavior, the properties of materials produced using control based on these principles tend to differ somewhat from the desired values of the actual properties as measured in the lab.
Where predictive models are used, the time required to identify the difference between predicted and measured polymer properties has led to various efforts to develop on-line instrumentation capable of measuring directly or inferring a product quality during polymer production. For example, it is known to use various on-line viscometers to directly measure rheometric properties of polymers. Alternatively, on-line instruments such as Fourier transform infrared spectrometers (“FTIRs”), near infrared spectrometers (“NIRs”), ultraviolet-visible (“UV-VIS”) spectrometers, Raman spectrometers and nuclear magnetic resonance spectrometers (“NMRs” or “IMRs”) have been used with varying degrees of success to infer material properties, such as melt flow rates (MFR), from the types of data that can be generated by these instruments and their associated data analysis software. Inferences of a property such as melt flow rate from spectrometric data typically is accomplished using advanced mathematical techniques such as multivariate curve fitting, neural networks, Principal Component Regression (PCR), or Partial Least Squares Regression Analysis (PLS), to transform the raw spectrometric data into an estimate of the desired physical property. Additional background information concerning PCR and PLS can be found in “Partial Least Squares Regression: A Tutorial”, Analytica Chimica Acta 185 (1986) 1-17, by P. Geladi and B. R. Kowalski.
In PCR and PLS, the spectrometric data are decomposed into two matrices, a “scores” matrix and a “loadings” matrix. The loadings matrix is a vector matrix containing the minimum number of vectors that adequately describe the variability in the spectral data while providing the desired level of predictive ability in the resulting model. The scores matrix is a scalar matrix that is calculated from each of the loadings vectors and each sample spectrum.
Thus, each sample spectrum in the calibration set can be reconstructed from a linear combination of the products of scores and loadings. For example, a four factor PCR or PLS model will have four loadings vectors and each sample can be described by four scalar values (the scores). A subset of one or more of these scores typically describes most of the variability attributable to a property such as melt flow rate. Additional information concerning the development and use of these techniques can be found in “Chemometrics: Its Role in Chemistry and Measurement Sciences”, Chemometrics and Intelligent Laboratory Systems, 3 (1988) 17-29, Elsevier Science Publishers B. V., and “Examining Large Databases: A Chemometric Approach Using Principal Component Analysis”, Journal of Chemometrics, Vol. 5, 163-179 (1991), John Wiley and Sons, both authored by Robert R. Meglen, the disclosure of each being incorporated by reference in its entirety.
“Coefficients” from multivariate curve fits and “weights” or “hidden node outputs” from neural network analysis are analogous concepts to the techniques discussed above and can also be used in combination with a process model.
In some cases, process control engineers have attempted to enhance spectral analyzer results by performing regression analysis of local process variables measured in-situ or in the immediate vicinity of the analyzer with scores resulting from the estimation of a property, such as Mooney viscosity, by on-line instrumentation. One such approach is described in U.S. Pat. No. 6,072,576 to McDonald, et al, the disclosure of which is hereby incorporated by reference. While this method may lead to improved process control in some cases, the industry desires new, more powerful approaches to integrating on-line instrumentation and process control. Such improved techniques would be useful, for example, to minimize variability in manufactured materials, or to minimize transition times when switching from the manufacture of one material to another. Ideally, these techniques could be used to improve the performance of plants using simpler control schemes such as PID or fuzzy logic control loops, as well as the performance of those plants using sophisticated predictive control models.